Created by:

• Bartosz Maślanka
• Kajetan Kubik

---

Wuhan blood raport

import pandas as pd
import numpy as np
import altair as alt
import seaborn as sns
from matplotlib import pyplot as plt
from plotnine import theme_dark, facet_grid, theme_classic, element_rect, element_line, geom_hline, geom_vline
from plotnine import ggplot, geom_point, aes, stat_smooth, facet_wrap, xlab, scale_x_log10, theme_bw, theme, element_text, theme_dark
import plotly.express as px
import plotly.offline as py
import plotly.graph_objs as go
import plotly
import plotly.figure_factory as ff
plotly.offline.init_notebook_mode()

---

Orginal data

data = pd.read_excel('wuhan.xlsx',engine = 'openpyxl')
data["PATIENT_ID"] = data["PATIENT_ID"].fillna(method='ffill')
new_data = data.groupby("PATIENT_ID").mean()
data

	PATIENT_ID	RE_DATE	age	gender	Admission time	Discharge time	outcome	Hypersensitive cardiac troponinI	hemoglobin	Serum chloride	...	mean corpuscular hemoglobin	Activation of partial thromboplastin time	High sensitivity C-reactive protein	HIV antibody quantification	serum sodium	thrombocytocrit	ESR	glutamic-pyruvic transaminase	eGFR	creatinine
0	1.0	2020-01-31 01:09:00	73	1	2020-01-30 22:12:47	2020-02-17 12:40:09	0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	1.0	2020-01-31 01:25:00	73	1	2020-01-30 22:12:47	2020-02-17 12:40:09	0	NaN	136.0	NaN	...	31.9	NaN	NaN	NaN	NaN	0.12	NaN	NaN	NaN	NaN
2	1.0	2020-01-31 01:44:00	73	1	2020-01-30 22:12:47	2020-02-17 12:40:09	0	NaN	NaN	103.1	...	NaN	NaN	43.1	NaN	137.7	NaN	NaN	16.0	46.6	130.0
3	1.0	2020-01-31 01:45:00	73	1	2020-01-30 22:12:47	2020-02-17 12:40:09	0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	1.0	2020-01-31 01:56:00	73	1	2020-01-30 22:12:47	2020-02-17 12:40:09	0	19.9	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6115	375.0	2020-02-16 11:21:00	68	1	2020-02-08 23:25:01	2020-02-19 01:31:58	1	84.9	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
6116	375.0	2020-02-16 12:04:00	68	1	2020-02-08 23:25:01	2020-02-19 01:31:58	1	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
6117	375.0	2020-02-16 12:14:00	68	1	2020-02-08 23:25:01	2020-02-19 01:31:58	1	NaN	NaN	105.2	...	NaN	NaN	267.0	NaN	139.3	NaN	NaN	17.0	88.6	77.0
6118	375.0	2020-02-16 14:11:00	68	1	2020-02-08 23:25:01	2020-02-19 01:31:58	1	NaN	155.0	NaN	...	31.6	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
6119	375.0	2020-02-16 14:37:00	68	1	2020-02-08 23:25:01	2020-02-19 01:31:58	1	NaN	NaN	NaN	...	NaN	35.8	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

6120 rows × 81 columns

---

Selecting correlated features

Selecting most correlated features with outcome. Selected features have correlation higher than 0.5 with outcome.

cor = new_data.corr().abs()
cor_target = abs(cor["outcome"])
relevant_features = cor_target[cor_target>0.5]
# relevant_features.sort_values()
df = pd.DataFrame(relevant_features).sort_values('outcome').drop(["outcome"])

fig = px.bar(df.reset_index(), x='outcome', y='index',
             hover_data=[], color='outcome',
             color_continuous_scale=px.colors.diverging.Geyser,
             title="Most correlated features")
fig.update_layout(
    xaxis={
        'title':'Corelation with outcome'},
    yaxis={'title':'Blood atribiutes'})
fig.show()

#Selecting highly correlated features
relevant_features = cor_target[cor_target>0.7].sort_values()
new_data = new_data.loc[:,relevant_features.index.insert(0,'gender').insert(0,'age')]
# new_data

Parallel coordinates plot, illustrating dependency between blood features which have correlation with outcome higher than 0.7.

fig = px.parallel_coordinates(
    new_data,
    color="outcome",
    labels = {"age":"Age",
              "gender":"Gender",
              "albumin":"Albiumn[g/dl]",
              "neutrophils(%)":"Neutrophils[%]",
              "(%)lymphocyte":"Lymphocyte[%]",
              "High sensitivity C-reactive protein":"High sensitivity C-reactive protein [mg/l]",
             },
    color_continuous_scale=px.colors.diverging.Geyser,

)
fig.update_layout(coloraxis_showscale=False)

# Show the plot
fig.show()

---

Heat map

By analyzing the markers for COVID detection, we decided to examine the correlation between them and the result, and then check how age affects the values blood features.

new_data = data[["PATIENT_ID","age","gender","outcome","Lactate dehydrogenase","High sensitivity C-reactive protein","(%)lymphocyte"]]
HSC = new_data.groupby("PATIENT_ID").mean()
plt.figure(figsize=(12,10))
cor = HSC.corr()
sns.heatmap(cor,annot=True,cmap=plt.cm.Reds)
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>

HSC['men_women'] = HSC['gender'].map({1: 'Men', 2: 'Women'})
HSC['recovered_dead'] = HSC['outcome'].map({0: 'Recovered', 1: 'Dead'})


brush = alt.selection_interval()
click = alt.selection_multi(encodings=['color'])


scale = alt.Scale(domain=['Recovered','Dead'],range=['rgb(0, 128, 128)','rgb(202, 86, 44)'])
color = alt.Color('recovered_dead:N', scale=scale,title='Outcome')
points = alt.Chart(HSC).mark_point(size=40).encode(
    alt.X('age:Q',title='Age'),
    alt.Y('High sensitivity C-reactive protein:Q',title="High sensitivity C-reactive protein [mg/l]"),
#     size = alt.Size('men_women:N',title='Gender'),
    color=alt.condition(brush, color, alt.value('lightgray')),
    shape = alt.Shape('men_women:N',title="Gender"),
    tooltip=['men_women:N','recovered_dead:N','age:N','(%)lymphocyte:N','Lactate dehydrogenase:N','High sensitivity C-reactive protein:N']
    
).add_selection(
    brush
    ).properties(
    width=1500,
).transform_filter(
    click
)

bars = alt.Chart(HSC).mark_bar().encode(
    x='count()',
    y=alt.Y('recovered_dead:N',title='Outcome'),
    color = alt.condition(click,color,alt.value('lightgrey')),
    
).add_selection(
    click
).transform_filter(brush).properties(
    width=1500,
)

alt.vconcat(
    points,
    bars,
    data=HSC,
    title="High sensitivity C-reactive protein"
)

---

brush = alt.selection_interval()
click = alt.selection_multi(encodings=['color'])

base = alt.Chart(HSC).mark_point(size=40).encode(
    y=alt.Y('age:Q',title='Age'),
#     size = alt.Size('men_women:N',title='Gender'),
    shape = alt.Shape('men_women:N',title="Gender"),

    color=alt.condition(brush, color, alt.value('lightgray')),
    tooltip=['men_women:N','recovered_dead:N','age:N','(%)lymphocyte:N','Lactate dehydrogenase:N','High sensitivity C-reactive protein:N']
    
).add_selection(
    brush
).properties(
#     width=400,
#     height=400
).transform_filter(
    click
)
# color = alt.Color('gender:N')
bars = alt.Chart(HSC).mark_bar().encode(
    x='count():Q',
    y=alt.Y('recovered_dead:N',title='Outcome'),
    color = alt.condition(click,color,alt.value('lightgrey')),

).add_selection(
    click
).transform_filter(brush).properties(
#     width=400
)
base.encode(x='Lactate dehydrogenase').properties(title='Lactate dehydrogenase [age/(IU/l)]') & bars | base.encode(x='High sensitivity C-reactive protein').properties(title='HSC [age/(mg/l)]') & bars | base.encode(x='(%)lymphocyte').properties(title='Lymphocyte [age/(%)]') & bars

data_1 = data.groupby("PATIENT_ID").mean()
# x_data = data_1
data_1['gender'].replace(1, 'Female',inplace=True)
data_1['gender'].replace(2, 'Male',inplace=True)
data_1['outcome'].replace(0, 'Recovered',inplace=True)
data_1['outcome'].replace(1, 'Dead',inplace=True)
#only to not mess with your data

def disc(x):
    if x < 25: return 'Young'
    elif x < 40: return 'Adult'
    elif x < 60: return 'Middle Age'
    else: return 'Senior'

age_data = data_1.sort_values(by = ['age'])
age_data['label_age'] = age_data['age'].apply(lambda x: disc(x))
age_data.label_age = pd.Categorical(age_data.label_age, ordered=True, categories = ['Young', 'Adult', 'Middle Age', 'Senior'])
# age_data

fig = px.scatter(age_data,
                 x = 'serum sodium',
                 y = 'thrombocytocrit',
                 color = 'outcome',
                 color_discrete_sequence=['rgb(0, 128, 128)','rgb(202, 86, 44)'],
                 facet_col = 'gender',
                 facet_row = 'label_age',
#                  range_y=[0, 0.6],
                 labels = {
                     "serum sodium":"Serum sodium [mmol/l]",
                     "thrombocytocrit":"Thrombocytocrit [%]"
                 },
                 template="plotly_white",
                 title="Comparing age groups, genders with Thrombocytocrit and Serum sodium."
                 ) 
fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))
fig.update_yaxes(title_font_size=9)
fig

Very high amount of blood samples of people, which albumin and calcium level was both below normal expected values(3.5 g/dl and 2.1 mmol/l respectively), which are represented by horizontal and vertical line, are in group of people of high risk.

fig = px.scatter(age_data,
                 x = 'calcium',
                 y = 'albumin',
                 color = 'outcome',
                 color_discrete_sequence=['rgb(0, 128, 128)','rgb(202, 86, 44)'],
                 template="plotly_white",
                title="Comapring albumin with calcium") 
fig.add_vline(x = 2.1, line_dash="dash", line_color="black")
fig.add_hline(y = 35, line_dash="dash", line_color="black")
fig.show()

def disc(x):
    for i in range(16):
        if x > bins[i][0] and x <= bins[i][1]:
            return bins[i][0]
            
bins = [[i, i+5] for i in range(15, 95, 5)]

age_data['disc_age'] = age_data['age'].apply(lambda x: disc(x))

names = age_data.columns
for name in names[3:-3]:
    age_data[name].fillna(age_data[name].mean(), inplace = True)
age_data

	age	gender	outcome	Hypersensitive cardiac troponinI	hemoglobin	Serum chloride	Prothrombin time	procalcitonin	eosinophils(%)	Interleukin 2 receptor	...	High sensitivity C-reactive protein	HIV antibody quantification	serum sodium	thrombocytocrit	ESR	glutamic-pyruvic transaminase	eGFR	creatinine	label_age	disc_age
PATIENT_ID
157.0	18	Male	Recovered	1.900000	127.000000	103.850000	14.600000	0.020000	0.566667	934.595293	...	0.650000	0.090000	143.500000	0.225000	4.500000	41.000000	215.450000	12.500000	Young	15
213.0	19	Female	Dead	12.800000	108.000000	97.500000	16.900000	0.130000	0.600000	934.595293	...	51.900000	0.070000	134.500000	0.214387	8.000000	11.000000	130.800000	69.000000	Young	15
102.0	22	Male	Recovered	1.900000	138.000000	100.800000	15.000000	0.030000	0.700000	582.000000	...	22.600000	0.099745	140.600000	0.200000	16.000000	19.000000	127.900000	55.500000	Young	20
200.0	25	Female	Recovered	765.964278	125.219553	102.412216	15.607362	0.880558	0.680637	934.595293	...	70.413724	0.099745	140.737974	0.214387	33.867593	38.709738	84.037712	NaN	Adult	20
195.0	26	Male	Recovered	1.900000	136.000000	98.200000	13.900000	0.020000	0.500000	447.000000	...	1.100000	0.150000	138.200000	0.300000	4.000000	16.000000	130.400000	48.000000	Adult	25
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
247.0	90	Female	Dead	1382.400000	110.250000	105.800000	20.300000	0.183333	0.100000	934.595293	...	79.150000	0.070000	138.850000	0.072500	47.000000	12.333333	76.666667	72.666667	Senior	85
313.0	91	Female	Dead	15.900000	104.500000	105.566667	15.150000	0.115000	0.000000	1190.000000	...	140.700000	0.060000	144.566667	0.160000	60.000000	20.000000	87.100000	54.333333	Senior	90
309.0	92	Female	Dead	141.600000	119.750000	116.533333	22.400000	1.010000	0.000000	513.000000	...	154.633333	0.090000	151.533333	0.130000	39.000000	42.666667	43.675000	132.750000	Senior	90
290.0	94	Male	Dead	9.900000	121.500000	97.800000	15.400000	0.485000	0.000000	934.595293	...	83.400000	0.099745	137.900000	0.155000	47.000000	12.000000	66.200000	69.000000	Senior	90
212.0	95	Female	Dead	280.700000	108.000000	109.250000	17.800000	0.600000	1.600000	2161.000000	...	78.000000	0.070000	142.100000	0.190000	80.000000	18.000000	26.300000	184.000000	Senior	90

375 rows × 79 columns